为进一步提高无线传感器网络(WSN)的定位精度,对锚节点分布与网络定位精度之间的关系进行研究,提出一种新的基于“聚集-共线度”(DAC)和“节点度”(ND)的锚节点选择算法—DAC-ND.首先,通过实验分析得出锚节点在共线分布和集中分布时对定位精度影响较大;然后,经过对基于共线度的锚节点选择算法进行分析和比较,发现现有的基于最小角和最小高的两类锚节点共线度算法(DC-A和DC-H)均存在不足;最后,综合这两类算法的优势提出一种新的基于“聚集-共线度”的概念,并结合“节点度”提出DAC-ND锚节点选择算法.通过Matlab仿真实验得出,与锚节点随机选择算法相比,DAC-ND算法可大幅降低平均定位误差(54％～73％);与基于最小角和最小高的共线度选择算法等相比,采用DAC-ND算法平均定位误差可分别降低15％～23％和12％～23％.实验结果表明,DAC-ND算法相比DC-A和DC-H能够获得更高的定位精度,从而验证了DAC-ND算法的有效性.%To improve the positioning accuracy of Wireless Sensor Network (WSN),the relationship between the positioning accuracy and the distribution of anchor nodes was studied,and a new anchor node selection algorithm based on the Degree of Aggregation-Collinearity (DAC) and Node Degree (ND) of anchors was proposed,namely DAC-ND.Firstly,the experimental analysis showed that the anchor nodes in collinear or concentrated distribution have a large influence on positioning accuracy.Secondly,by analyzing and comparing the anchor node selection algorithms based on Degree of Collinearity (DC),it was found that two kind of anchor node selection algorithms based on the minimum angle (DC-A) or minimum height (DC-H) have some disadvantages.Finally,combining the advantages of the above both algorithms,the new concept of DAC was proposed,and the DAC-ND algorithm was put forward based on DAC and ND.The Matlab simulation results illustrated that,the average position error of DAC-ND can be greatly reduced by 54％-73％ compared with the random selection algorithm,and it also can be reduced by 15％-23％ and 12％-23％ compared with the anchor selection algorithms based on DC-A and DC-H respectively.The experimental results demonstrate that the DAC-ND algorithm can obtain much higher positioning accuracy compared with algorithms based on DC-A or DC-H,which proves the effectiveness of the DAC-ND algorithm.
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